nco | Configurable NCO

When I save an entity that has a @JoinColumn field that references another entity, it is saved correctly as expected by calling saveAndFlush(). Now, I want to be able to return this entity along with its related entities back to the user. I assumed that calling getById() with the ID of the newly saved entity will also retrieve @JoinColumn values in the returned entity, however, the related entity of the returned entity contains the exact same values as the related entity that used in saveAndFlush(). I have made example code to demonstrate what I'm talking about.

I send request: GET http://localhost:8080 and receive as response:

I have a dataframe column that has an input like below.

i want filter all data on condition type have contains() or subset() 'NCO - ETD' follow groupby date and id.

I wrote this code:

I'm creating a Database from excel files scraped from the web, the problem is that the source lacks a pattern, the name of the columns vary a lot and sometimes it exceeds the 64 characters limitation from MySQL. My solution by now is running the script and replace each word I find to a reduced version. The problem is that there is too much data and the replace library is getting huge.

Here's an example of one table being created after replaces:

Others have asked about this, but my situation seems slightly different, and none of the suggestions they received worked for me (e.g. here, here, here).

I'm using Anaconda Navigator on Windows, and trying to use the "nco" package. I installed it via the Anaconda Navigator, and when (in Spyder) I type conda list nco it gives me:

I have a number of coordinates (roughly 20000) for which I need to extract data from a number of NetCDF files each comes roughly with 30000 timesteps (future climate scenarios). Using the solution here is not efficient and the reason is the time spent at each i,j to convert "dsloc" to "dataframe" (look at the code below). ** an example NetCDF file could be download from here **

I calculated the potential temperaure from a NetCDF file. I would like to change standard_name and long_name with NCO. I have tried some commands without sucess, e.g.:

Currently I use global precipitation (ppt) and potential evapotranspiration (pet) data to calculate SPEI. As I have limited hardware resources, I divided global ppt and ppt data into 32 parts, each file covering 45x45deg and contains 756 data - monthly from 1958-2020 (tile01.nc, tile02.nc, ... tile32.nc)

• For example to do this, I use cdo sellonlatbox,-180,-135,45,90 in.nc out.nc or ncks -d lat,45.,90. -d lon,-180.,-135. in.nc -O out.nc
• As required by SPEI script, I reorder and fixed the dimension from time,lat,lon to lat,lon,time using ncpdq and ncks.
• From the SPEI output, I got the output in lat,lon,time. So I did reorder the dimension so that it becomes time,lat,lon using ncpdq.
• Each tile SPEI output covering 45x45deg and contains 756 SPEI data - monthly from 1958-2020

Finally I need to merge all the output together (32 files) into one, so I will get the global SPEI output. I have try to use cdo mergegrid but the result is not what I expected. Is there any command from cdo or nco to solve this problem that has function similar to gdal_merge if we are dealing with geoTIFF format?

Below is the example of the SPEI output

UPDATE

I managed to merge all the data using cdo collgrid as suggested by Robert below. And here's the result:

I have 4-dimensional data (time, depth, y, and x), but the latitude and longitude are both 2d arrays. y and x are just the indices, so just integers going from 0, 1...end etc. Very similar to the example data set provided by MetPy:

https://unidata.github.io/MetPy/latest/examples/cross_section.html

Unfortunately this is is not the most reproducible, because it's very specific to the data itself. But I am having trouble at the cross section part. I can parse the data according to metpy, but then I get an error when taking a cross section: